Single-stage Methods   More recent efforts in NCD [1]}, [2]}, [3]} use labeled and the unlabeled data together to discover novel categories. RS [1]}, [5]}, NCL [2]} and OpenMix [7]} first use RotNet [8]} to self-supervise on the labeled and unlabeled data. Then, RS [1]} uses pseudo-labels from ranking-statistics method to learn an unlabeled head. NCL [2]} and Jia [11]} find that contrastive learning improves class discovery and OpenMix [7]} uses mix-up [13]} to generate more training data to guide class discovery. UNO [3]} finds that a unified loss function enhances the synergy between the learnings from labeled and the unlabeled data. Zhao and Han [15]} proposes to focus on fine-grained local cues in images to enhance discriminationAs NCD is a nascent field, we will maintain an updated list of methods here: https://github.com/JosephKJ/Awesome-Novel-Class-Discovery..
